Identifying pavement damage is a crucial component in road maintenance and infrastructure management. Prompt detection and corrective action of pavement defects can prevent severe deterioration, maintain safety, and prolong the useful life of road infrastructure. Computer vision is widely applied to vehicle applications, such as driver assistance and self-driving, and that is a good platform to equip the pavement damage recognition service. Therefore, this work applies computer vision to develop the pavement damage detection service named high-performance pavement health recognition (HPPH) to detect common pavement damages, including longitudinal cracks, transverse cracks, alligator cracks, and potholes. The proposed HPPH can be deployed on a vehicle for regular road detection. To fit the requirements of vehicle applications, HPPH considers state-of-the-art techniques to optimize the performance of the recognition service on vehicles, e.g., DeepStream is applied to increase the inference performance. At the same time, YOLOv8s (you only look once, YOLO) provides real-time inference. The experimental results reveal that HPPH provides an accuracy of 90.3% and an average precision 0.5 of 74.3%. Moreover, the optimized HPPH provides three times better than the pure YOLOv8s in terms of the inference speed. In summary, the proposed HPPH provides high recognition accuracy with high efficiency and is feasible to be applied to vehicle applications to realize distributed road maintenance.

Intelligent systems influence many aspects of daily life. With the emergence of the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), opportunities have been created for smart computing infrastructure. However, problems might arise from the lack of interconnectivity, higher reliability, real-time predictive analytics, and low-latency requirements. Therefore, in this article, we propose the sensors, edge, and cloud (iSEC) framework. The project deploys a smart cloud edge-computing architecture to provide ML and deep learning in the cloud edge environment. Two pilot projects of air quality monitoring system and object detection are demonstrated to evaluate the iSEC framework.